To accomplish tasks in human-centric indoor environments, agents need to represent and understand the world in terms of objects and their attributes. We consider how to acquire such a world model via noisy perception and maintain it over time, as objects are added, changed, and removed in the world. Previous work framed this as multiple-target tracking problem, where objects are potentially in motion at all times. Although this approach is general, it is computationally expensive. We argue that such generality is not needed in typical world modeling tasks, where objects only change state occasionally. More efficient approaches are enabled by restricting ourselves to such semi-static environments. We consider a previously-proposed clustering-based world modeling approach that assumed static environments, and extend it to semi-static domains by applying a dependent Dirichlet process (DDP) mixture model. We derive a novel MAP inference algorithm under this model, subject to data association constraints. We demonstrate our approach improves computational performance for world modeling in semi-static environments.